The present invention relates to the field of computing resource identification and, more particularly, to a technique for granular identification of computing resources based on computing resource DNA.
Computing systems often include a wide variety of software and hardware topologies and technologies, such as virtual machines and distributed architectures. These complex configurations result in the same physical component, called a computing resource, being referenced by other software and hardware in the computing system in different manners. For example, a network hub may reference a desktop computer by its Media Access Control (MAC) address, whereas a Web service may reference the same desktop computer by its Internet Protocol (IP) address.
As such, software applications and hardware devices that handle or monitor the communication traffic are prone to recognizing a single computing resource as multiple, separate computing resources. For example, a load-balancing algorithm may be unable to determine the actual load of a server that is running multiple virtual machines because message traffic for each virtual machine is recognized as separate physical machines. Thus, the overall performance of the computing system is degraded.
Attempts to overcome this problem revolve around static correlations for a few basic identifying characteristics of the computing resources. While such approaches can help to alleviate this problem, the manpower required for the creation and maintenance of the characteristics and correlations is often prohibitive. That is, these manual attempts are too labor intensive for large and/or complex computing systems.